TransXplorer

Unlock the Power of RNA-seq Analysis
From Raw Data to Therapeutic Breakthroughs

Why Choose TransXplorer?

End-to-End RNA-seq Pipeline

Seamlessly process raw FASTQ files to actionable biological insights with automated quality control and advanced normalization.

DESeq2 edgeR limma-voom QC Automation

Network Analysis Suite

Explore co-expression, protein-protein interactions, and gene regulatory networks with interactive visualizations.

WGCNA STRING-DB Gene Networks PPI Analysis

Therapeutic Discovery

Identify drug targets with integrated access to ChEMBL, OpenTargets, and drug-gene interaction networks.

ChEMBL OpenTargets Drug Discovery Therapeutics

Clinical Insights

Validate findings with TCGA data, survival analysis, and cell-type deconvolution for clinical relevance.

TCGA Survival Analysis Cell Deconvolution Biomarkers

Dynamic Visualizations

Create interactive volcano plots, heatmaps, PCA, UMAP, and pathway networks with real-time filtering.

Plotly Interactive Plots Real-time Exportable

Multi-Omics Support

Analyze human, mouse, rat, and other model organisms with automatic ID mapping and multi-omics integration.

Multi-omics Human Mouse ID Conversion

Powerful Analysis Modules

Differential Expression

Perform robust DEG analysis with multiple statistical methods and advanced filtering options.

  • DESeq2, edgeR, limma-voom
  • Batch effect correction
  • Interactive volcano plots

Pathway Enrichment

Uncover biological pathways using KEGG, Reactome, GO, and GSEA databases.

  • GSEA and ORA analysis
  • Pathway network visualization
  • Cross-pathway interactions

Clinical Insights

Validate findings with TCGA data, survival analysis, and patient stratification tools.

  • Kaplan-Meier survival plots
  • Biomarker identification
  • Prognostic modeling

WGCNA Analysis

Identify co-expression modules and hub genes from expression data.

  • Module-trait correlations
  • Hub gene identification
  • Interactive network plots

Regulatory Networks

Explore protein-protein interactions (PPI) and gene regulatory networks (GRN).

  • STRING-DB integration
  • DoRothEA TF database
  • Master regulator analysis

Cell type & Drug Discovery

Carrying cell-type deconvolution and identify potential drug targets.

  • Cell-type deconvolution
  • ChEMBL & OpenTargets
  • Drug prioritization

Your Research Workflow, Streamlined

1

Upload Data

Import FASTQ, count matrices, or pre-processed data with automatic format detection and validation.

2

Quality Control

Run automated QC with interactive reports and tailored filtering recommendations.

3

Differential Analysis

Perform statistical analysis with multiple methods and visualize results interactively.

4

Biological Insights

Explore pathways, networks, and functional annotations for significant genes.

5

Therapeutic Discovery

Generate therapeutic hypotheses with drug-target identification and clinical validation.

TransXplorer Impact

35+
TCGA Cancer Types
1500+
Analyzed Pathways
8
Analysis Modules
99.9%
Automation Level

Accelerate Your RNA-seq Discoveries

Join thousands of researchers using TransXplorer to transform RNA-seq data into actionable therapeutic insights. Start your journey today with our seamless, AI-powered platform.

Developed by Varinder Madhav Verma, Andrew L. Mason, David Wishart, Gane Ka-Shu Wong @ University of Alberta

Contact: varinde2@ualberta.ca | transxplorer.org

RNA-seq Analysis Pipeline

Transform your raw sequencing data into meaningful biological insights

Analysis Pipeline Overview

1
Quality Control

FastQC analysis of raw reads

2
Read Trimming

Trimmomatic quality filtering

3
Alignment

HISAT2 genome mapping

4
Quantification

featureCounts gene counting

5
Quality Metrics

Comprehensive QC reporting

Gene Expression Count Matrix

Waiting for analysis...

Real-time Processing Log

Analysis in Progress...

Please wait while we process your FASTQ files

FastQC Quality Control Reports

Select Report

Download Report

No QC Reports Available

QC reports will appear here after FASTQ processing is complete.

Quality Control Summary

About This Analysis

🧰 Tools Used:
  • FastQC: Quality assessment of raw sequencing data
  • Trimmomatic: Adapter removal and quality trimming
  • HISAT2: Fast and sensitive alignment to reference genome
  • featureCounts: Efficient read counting for gene expression quantification
📋 Output Files:
  • Count Matrix: Gene × Sample expression counts (CSV format)
  • QC Report: Comprehensive quality metrics and visualizations
  • Summary Stats: Alignment rates, feature assignment statistics
🔄 Next Steps:

After processing, use the count matrix in the 'Transcriptome Analysis' section for differential expression analysis, exploratory analysis, pathway enrichment, and advanced analysis.

Differential Gene Expression Analysis

Discover significant genes and biological pathways from your expression data

Data Upload

Count matrix or expression data

Statistical Analysis & Visualization

DEG analysis with interactive plots

Pathway Enrichment

GO, KEGG & biological interpretation

Systems Biology

PPI networks, drug targets & gene modules

Processing...
Analysis in progress...

Exploratory Data Analysis


Principal Component Analysis (PCA)


UMAP Projection



Batch Effect Assessment

Compare dimensionality reduction plots before and after batch correction to verify effectiveness.

PCA Before Batch Correction
PCA After Batch Correction

UMAP Before Batch Correction
UMAP After Batch Correction

How to interpret: After correction, samples should cluster more by biological group rather than batch. If batch clustering persists after correction, consider stronger methods or check for confounding.


About these plots: PCA and UMAP are dimensionality reduction techniques that help visualize sample similarities and differences. Samples that cluster together are more similar in their gene expression profiles.


Batch Visualization Available: Use the toggle buttons above to switch between 'Group' and 'Batch' coloring. If samples cluster by batch rather than biological groups, batch correction may be needed in your analysis.

Human Samples Only
Cell type deconvolution is currently only available for human samples. Please select 'human' as your organism in the sidebar to enable this feature.
No Cell Type Data Available

To analyze cell type composition:
  1. Check the 'Include Cell Type Composition' checkbox in the sidebar
  2. Select your preferred deconvolution method (xCell, MCP-counter, or EPIC)
  3. Click the 'Run Exploratory QC' button

Cell Type Proportions per Sample


Detailed Composition Summary

Download CSV

Interpretation: The bar plot shows the relative proportions of different cell types across your samples. The table provides detailed statistics including mean enrichment scores, standard deviations, and statistical significance values. Cell types highlighted in green/blue/yellow have significant enrichment (p < 0.05, 0.01, or 0.001 respectively).

Cell Type Enrichment Heatmap


About the heatmap: This heatmap displays scaled enrichment scores (z-scores) for each cell type across samples.
  • Red colors: High enrichment of the cell type
  • Blue colors: Low/depleted enrichment
  • White: Average enrichment
  • Vertical dashed lines separate different sample groups (if present)

Differentially Expressed Genes

💡 Tip: Click on any gene row to view detailed information from OpenTargets database.
Download All DEGs
💡 Tip: Click on any gene row to view detailed information from OpenTargets database.
Download Upregulated
💡 Tip: Click on any gene row to view detailed information from OpenTargets database.
Download Downregulated

Volcano Plot

Download Plot

DEG Heatmap

Gene Selection
Sample Filtering
Uncheck to show only the two compared groups
Direction Filter
Number of Genes
Search and select genes from your current DEG results. Showing gene names with IDs.
Clustering Method
Row Clustering
Column Clustering
Download Heatmap

📊 Current Analysis Overview

🧬 Organism & Database Selection

Auto-detected from DEG Analysis:
Database selection is automatically filtered based on your organism.

⚙ Pathway Analysis Parameters

📈 Pathway Filtering Thresholds
Note: These are separate from your DEG analysis thresholds and are used to filter pathway results.

📋 Pathway Enrichment Results

No Results Yet

Run pathway enrichment analysis to see results here.

📈 Analysis Statistics
Network Controls

Download PNG
💡 Quick Guide: ■ Boxes = Pathways (colored by database) | ● Circles = Genes | Click & drag nodes to reposition | Scroll to zoom | Hover for details

Select Your Analysis Type

Drug-Target Interactions
Analysis Results

✅ Analysis completed! Click tabs below to explore detailed results.

PPI Analysis Results

✅ Protein-protein interaction network analysis completed!

GRN Analysis Results

✅ Gene regulatory network analysis completed!

🧬 WGCNA Co-expression Network Analysis Results

✅ WGCNA analysis completed! Results will persist until you run a new analysis.

Modules

Hub Genes

Genes

Quality R²

✅ WGCNA analysis completed! Click tabs below to explore modules and hub genes.

Ready to Analyze

Configure parameters and click 'Run Analysis' to get started.

Gene selection: Ready
Parameters: Configured
Status: Waiting for analysis

Disease Context Analysis Overview

This analysis focuses on clinical evidence and disease associations from OpenTargets database.

Disease Targets
Approved Drugs
High Safety Score
Clinical Evidence

Disease Context Summary


Disease-Based Drug Prioritization

Drugs prioritized based on clinical evidence, safety profiles, and disease relevance.


Clinical Development Pipeline

Overview of drugs by clinical development phase and therapeutic area.


Detailed Clinical Evidence


Drug Safety Profile Analysis

Safety scores and risk assessment for identified drugs.


Top Priority Results


Drug Prioritization Analysis

This table shows drugs ranked by priority score, with each row representing a unique drug and its aggregated properties across all target interactions.


Complete Drug-Target Interaction Dataset

This table shows all individual drug-target interactions found, with each row representing a specific drug-gene pair. Use this for detailed analysis of specific interactions.


Save Image

Comprehensive Analysis Results

Multi-database analysis combining ChEMBL and OpenTargets data for complete drug-target landscape assessment.


Cross-Database Analysis Summary

Combined insights from ChEMBL bioactivity data and OpenTargets clinical intelligence.

Total Unique Drugs
Cross-Validated Targets
Database Coverage
Clinical Stage Potential
Key Insights

Cross-Database Validation Summary

Cross-Validated Targets (High Confidence)

Targets found in both ChEMBL and OpenTargets databases represent the highest confidence drug opportunities.


Bioactive Compound Analysis

Drug-target interactions with focus on bioactivity measurements and chemical properties.


Clinical and Disease Context

Target druggability, clinical trials, and disease associations from OpenTargets.


Multi-Database Network View

Interactive network showing relationships from both ChEMBL and OpenTargets data.

Download

Comprehensive Drug Ranking

Multi-factor prioritization combining bioactivity, clinical evidence, and target coverage.

Download

🎯 Network Quality Assessment

Modules
Hub Genes
Quality R²
Genes

📋 Quality Diagnostics

                                            

                                          

                                        

                                      

                                      

                                        

                                          

                                            
📈 Module Summary

                                          

                                          

                                            

                                          

                                        

                                      

                                    

                                  

                                  

                                    

                                      

                                        
                                         Evidence-Based Module Categorization
                                      

                                      
Modules categorized by biological function using weighted pathway enrichment evidence

                                    

                                    

                                      

                                        

                                          

                                            
                                          

                                          
Categorized Modules

                                        

                                      

                                      

                                        

                                          

                                            
                                          

                                          
High Confidence

                                        

                                      

                                      

                                        

                                          

                                            
                                          

                                          
Avg Confidence Score

                                        

                                      

                                      

                                        

                                          

                                            
                                          

                                          
Pathways Analyzed

                                        

                                      

                                    

                                    

                                    

                                      

                                        

                                          

                                            
                                             Module Category Summary
                                          

                                          
Each module is assigned a primary category based on weighted evidence from enriched pathways. 
          Confidence scores reflect the strength of the assignment.

                                          

                                            

                                              

                                                
                                                

                                                  
                                                  
                                                

                                              

                                            

                                            

                                              

                                                
                                                

                                                  
                                                  
                                                

                                              

                                            

                                            

                                              
                                                
                                                Download Table
                                              
                                            

                                          

                                          

                                          

                                        

                                      

                                    

                                    

                                    

                                      

                                        

                                          

                                            
                                             Detailed Module Analysis
                                          

                                          

                                            

                                              

                                                
                                                

                                                  
                                                  
                                                

                                              

                                            

                                            

                                              

                                                

                                              

                                            

                                          

                                          

                                          

                                            

                                              
Category Confidence Breakdown

                                              

                                            

                                            

                                              
Top Hub Genes & Supporting Pathways

                                              

                                                

                                              

                                            

                                          

                                        

                                      

                                    

                                    

                                    

                                      

                                        

                                          

                                            
                                             Category Distribution Across All Modules
                                          

                                          

                                        

                                      

                                    

                                    

                                    

                                      

                                        
                                         Methodology
                                      

                                      
                                      
                                    

                                  

                                  

                                    

                                      

                                        
🎯 Hub Gene Analysis

                                      

                                      

                                        

                                          

                                            

                                              
                                              

                                                
                                                
                                              

                                            

                                          

                                          

                                            

                                              

                                                
                                              

                                            

                                          

                                          

                                            

                                              
                                              
                                            

                                          

                                          

                                            
                                              
                                              Download Hub Genes
                                            
                                          

                                        

                                        

                                        

                                      

                                    

                                  

                                  

                                    

                                      

                                        

                                          
                                           Module-Trait Correlation Analysis
                                        

                                        
Correlations between co-expression modules and selected sample traits

                                      

                                      

                                        

                                          
                                           How to Interpret Results
                                        

                                        

                                          

                                            

                                              
                                               Correlation Values:
                                            

                                            
    
                                              
  • Positive (+, Red): 
                                            Module expression increases as trait value increases
    • 
                                              
  • Negative (−, Blue): 
                                            Module expression decreases as trait value increases
    • 
                                              
  • |r| ≥ 0.7: Very strong (biologically important)
    • 
                                              
  • |r| = 0.4–0.7: Moderate relationship
    • 
                                              
  • |r| < 0.4: Weak or no relationship
    • 
                                            

                                          

                                          

                                            

                                              
                                               Reading the Sign:
                                            

                                            
    
                                              
  • Check your trait encoding in the table above
    • 
                                              
  • Positive correlation: genes higher when trait = higher value
    • 
                                              
  • Negative correlation: genes higher when trait = lower value
    • 
                                              
  • * (asterisk): Statistically significant (p < 0.05)
    • 
                                              
  • Focus on hub genes in significant modules
    • 
                                            

                                          

                                        

                                        

                                          

                                            
                                             Example Interpretations:
                                          

                                          

                                            
📊 Binary Trait Example: Sex

                                MEpink ↔ sex = +0.77* (encoding: female=0, male=1)

                                Interpretation: This module's genes are highly expressed in males. 
                                The positive correlation means expression increases with trait value (male=1). 
                                Hub genes in this module likely represent sex-specific pathways 
                                or hormone-responsive genes that are more active in males.

                                          

                                          

                                            
📈 Continuous Trait Example: Age or Dose

                                MEblue ↔ age = +0.65* (age in years: 20, 30, 40, 50...)

                                Interpretation: This module's expression increases with age. 
                                As subjects get older, these genes become more active. This could represent 
                                aging-related processes, senescence pathways, or accumulated 
                                changes over time. Higher correlation = stronger age-dependency.

                                          

                                        

                                        

                                          
⚡ Quick Guide: 
                            (1) Find significant modules (marked with *), 
                            (2) Check trait encoding to understand which group/value is higher, 
                            (3) Positive = genes higher in higher-value group, Negative = genes higher in lower-value group, 
                            (4) Examine hub genes for mechanistic insights.

                                        

                                      

                                      

                                        

                                          

                                            

                                              
                                            

                                            
Traits Analyzed

                                          

                                        

                                        

                                          

                                            

                                              
                                            

                                            
Samples Matched

                                          

                                        

                                        

                                          

                                            

                                              
                                            

                                            
Significant (p<0.05)

                                          

                                        

                                        

                                          

                                            

                                              
                                            

                                            
Strongest |r|

                                          

                                        

                                      

                                      

                                      

                                        

                                          
                                           Selected Traits Information
                                        

                                        

                                      

                                      

                                        

                                          
                                           Correlation Heatmap
                                        

                                        
Cell values show Pearson correlation coefficients between module eigengenes and traits. * indicates p < 0.05

                                        

                                        
                                          
                                          Download Heatmap
                                        
                                      

                                      

                                        

                                          
                                           Significant Module-Trait Associations
                                        

                                        
Associations with p < 0.05, sorted by correlation strength.

                                        

                                        
                                          
                                          Download Full Correlation Matrix
                                        
                                      

                                    

                                    

                                      

                                        
                                        
Module-Trait Analysis Not Available

                                        
Enable trait analysis in the parameters and upload metadata to see correlations.

                                      

                                    

                                  

                                  

                                    

                                      

                                        

                                          
🔬 Module Functional Enrichment

                                        

                                        

                                          

                                            

                                              

                                                
                                                

                                                  
                                                  
                                                

                                              

                                            

                                            

                                              

                                                
                                                

                                                  
                                                  
                                                

                                              

                                            

                                            

                                              

                                                
                                                
                                              

                                            

                                            
                                          

                                          

                                          

                                        

                                      

                                    

                                    

                                      

                                        
                                        

                                        

                                        
Functional Enrichment Not Available

                                        
Enrichment analysis was not performed or no significant results found.

                                        
Try enabling enrichment in the analysis parameters or check if enrichR package is installed.

                                      

                                    

                                  

                                  

                                    

                                      

                                        
📊 WGCNA Analysis Information

                                      

                                      

                                        

                                        

                                          

                                            
📁 Download Options

                                            
                                              
                                              Module Assignments (.csv)
                                            
                                            

                                            
                                              
                                              Hub Genes (.csv)
                                            
                                            

                                            
                                              
                                              Network (.json)
                                            
                                          

                                          

                                            
🔧 Analysis Parameters

                                            

                                          

                                        

                                      

                                    

                                  

                                

                              

                            

                            

                              

                                

                                  

                                    

                                      

                                        

                                      

                                      
Cell Types Detected

                                    

                                  

                                  

                                    

                                      

                                        

                                      

                                      
Dominant Cell Type

                                    

                                  

                                  

                                    

                                      

                                        

                                      

                                      
Confidence Score

                                    

                                  

                                  

                                    

                                      

                                        

                                      

                                      
DEG Markers Identified

                                    

                                  

                                

                                

                                

                                  

                                    

                                  

                                

                                

                                

                                  
                                  

                                    

                                      

                                      

                                        

                                          

                                        

                                      

                                      

                                      

                                        

                                          

                                            

                                              

                                                
                                                 Cell Type Proportions Across Samples
                                              

                                            

                                            

                                              

                                            

                                          

                                        

                                      

                                      

                                      

                                        

                                          

                                            

                                              

                                                
                                                 Cell Type Composition Table
                                              

                                              
                                                
                                                Download CSV
                                              
                                            

                                            

                                              

                                            

                                          

                                        

                                      

                                    

                                    

                                      

                                      

                                        

                                          

                                            
                                             Cell Type Enrichment Heatmap
                                          

                                        

                                        

                                          

                                          

                                          

                                            
                                             Values are scaled (z-score) for visualization. 
                                            Blue = depleted, Red = enriched.
                                          

                                        

                                      

                                    

                                    

                                      

                                      

                                        

                                          

                                            
                                             Cell-Type-Specific DEG Markers
                                          

                                          
                                            
                                            Download CSV
                                          
                                        

                                        

                                          

                                            
                                             This table shows which of your differentially expressed genes are markers for specific cell types.
                                          

                                          

                                        

                                      

                                    

                                    

                                      

                                      

                                        

                                          

                                            
                                             About this analysis: 
                                            Compare cell type proportions between your sample groups (e.g., control vs tumor). 
                                            Significant differences suggest group-specific cell type enrichment.
                                          

                                        

                                      

                                      

                                        

                                          

                                            

                                              
                                               Display Options
                                            

                                            

                                              
                                              

                                                

                                                  
                                                

                                                

                                                  
                                                

                                                

                                                  
                                                

                                              

                                            

                                            

                                              

                                                
                                                

                                                  
                                                  
                                                

                                              

                                            

                                            

                                              

                                                
                                                
                                              

                                            

                                            

                                            

                                              
                                              
                                            

                                            

                                              
                                              

                                                
                                                
                                              

                                            

                                            

                                            

                                              
                                               Quick Stats
                                            

                                            

                                          

                                        

                                        

                                          

                                            

                                              

                                                
                                                 Cell Type Proportions by Group
                                              

                                            

                                            

                                              

                                            

                                          

                                        

                                      

                                      

                                      

                                        

                                          

                                            

                                              

                                                
                                                 Statistical Comparison Results
                                              

                                            

                                            

                                              

                                            

                                          

                                        

                                      

                                    

                                    

                                      

                                      

                                        

                                          
                                           Interpretation Guide
                                        

                                        

                                        
Understanding Your Results

                                        

                                          

                                            
                                             What do the scores mean?
                                          

                                          
    
                                            
  • 
                                              xCell: 
                                              Enrichment scores (not proportions). Higher scores indicate higher presence/activity of that cell type. 
                                              Scores are relative and should be compared across samples.
                                            
    • 
                                            
  • 
                                              MCP-counter: 
                                              Absolute scores that correlate with cell abundance. 
                                              Higher values = more of that cell type present.
                                            
    • 
                                            
  • 
                                              EPIC: 
                                              Actual proportions (0-1). Values represent the estimated fraction of each cell type in the sample.
                                            
    • 
                                          

                                        

                                        

                                          

                                            
                                             Key Interpretation Tips
                                          

                                          
    
                                            
  1. 
                                              Compare, don't interpret in isolation: 
                                              Cell type scores are most meaningful when comparing between samples or groups.
                                            
    2. 
                                            
  2. 
                                              Check confidence score: 
                                              Scores above 70% indicate reliable results. Below 50% suggests high variability.
                                            
    3. 
                                            
  3. 
                                              Dominant cell type: 
                                              The cell type with the highest average proportion across all samples. 
                                              This gives you a quick overview of your tissue composition.
                                            
    4. 
                                            
  4. 
                                              DEG markers: 
                                              If your upregulated DEGs are enriched in a specific cell type, 
                                              it suggests that cell type may be driving your observed changes.
                                            
    5. 
                                          

                                        

                                        

                                          

                                            
                                             Important Limitations
                                          

                                          
    
                                            
  • Deconvolution estimates are computational predictions, not direct measurements
    • 
                                            
  • Results depend on the quality of reference signatures used by each method
    • 
                                            
  • Low RNA quality or very low cell counts can reduce accuracy
    • 
                                            
  • For validation, consider orthogonal methods (flow cytometry, IHC, etc.)
    • 
                                            
  • EPIC requires tumor samples; don't use it for normal tissue
    • 
                                          

                                        

                                        

                                          

                                            
                                             Recommended Next Steps
                                          

                                          
    
                                            
  1. Examine the heatmap to identify sample clustering by cell composition
    2. 
                                            
  2. Check if cell type differences explain your experimental groups
    3. 
                                            
  3. Link DEG markers to cell types to understand which cells drive expression changes
    4. 
                                            
  4. Validate key findings with literature or orthogonal experimental methods
    5. 
                                            
  5. Export results for publication-quality figures in external software
    6. 
                                          

                                        

                                        

                                          
                                           Pro Tip: 
                                          If you see a strong correlation between a cell type and your experimental condition, 
                                          consider whether the biological effect is due to changes in that cell type's abundance 
                                          or changes in gene expression within existing cells.
                                        

                                      

                                    

                                  

                                

                              

                            

                            

                              

                                

                                  

                                    
                                    
Waiting for PPI Analysis

                                    
Please wait while the protein-protein interaction network is being analyzed...

                                  

                                

                                

                                  

                                    
                                    PPI Analysis Complete! Results are ready for viewing.
                                  

                                  

                                    
                                    

                                      

                                        

                                          

                                            

                                              

                                                

                                                  

                                                    
                                                  

                                                  

                                                    
                                                  

                                                  

                                                    
                                                  

                                                  

                                                    
                                                      
                                                      💾 PNG
                                                    
                                                  

                                                  

                                                    
Network auto-stabilizes after 10 seconds

                                                  

                                                

                                              

                                            

                                          

                                          

                                            

                                              

                                                

                                              

                                            

                                          

                                          

                                            

                                              

                                                
Network Statistics

                                                

                                                  

                                                    

                                                      

                                                        

                                                      

                                                      
Proteins

                                                    

                                                  

                                                  

                                                    

                                                      

                                                        

                                                      

                                                      
Interactions

                                                    

                                                  

                                                  

                                                    

                                                      

                                                        

                                                      

                                                      
Density

                                                    

                                                  

                                                  

                                                    

                                                      

                                                        

                                                      

                                                      
Hub Proteins

                                                    

                                                  

                                                

                                              

                                            

                                          

                                        

                                      

                                      

                                        

                                          

                                            

                                              

                                                
Network Hub Proteins

                                                
Hub proteins are highly connected nodes that play crucial roles in the network.

                                                

                                                

                                                
                                                  
                                                  Download Hub Proteins
                                                
                                              

                                            

                                          

                                        

                                      

                                      

                                        

                                          

                                            

                                              

                                                
Protein-Protein Interactions

                                                

                                                

                                                
                                                  
                                                  Download Interactions
                                                
                                              

                                            

                                          

                                        

                                      

                                      

                                        

                                          

                                            

                                              

                                                
Network Statistics

                                                
                                              

                                              

                                                

                                                  
                                                   Network Statistics Explained
                                                

                                                
These metrics describe the overall structure of your protein interaction network:

                                                
    
                                                  
  • 
                                                    Total Proteins:
                                                     Number of proteins in the network
                                                  
    • 
                                                  
  • 
                                                    Total Interactions:
                                                     Number of connections between proteins
                                                  
    • 
                                                  
  • 
                                                    Coverage Rate:
                                                     Percentage of your input genes found in the interaction database
                                                  
    • 
                                                  
  • 
                                                    Network Density:
                                                     How interconnected the network is (0 = sparse, 1 = fully connected)
                                                  
    • 
                                                  
  • 
                                                    Average Degree:
                                                     Average number of interactions per protein (higher = more connected)
                                                  
    • 
                                                

                                              

                                              

                                            

                                            

                                            

                                              

                                                
Confidence Distribution

                                                
                                              

                                              

                                                

                                                  
                                                   Confidence Score Explained
                                                

                                                

                                                  What is Confidence Score?
                                                   Each protein interaction has a confidence score (0-1) indicating reliability based on experimental evidence, databases, and predictions.
                                                

                                                

                                                  Score Interpretation:
                                                

                                                
    
                                                  
  • 
                                                    0.9 - 1.0:
                                                     Very strong evidence - highly reliable
                                                  
    • 
                                                  
  • 
                                                    0.7 - 0.9:
                                                     Good evidence - likely biologically relevant
                                                  
    • 
                                                  
  • 
                                                    0.4 - 0.7:
                                                     Moderate evidence - consider validating
                                                  
    • 
                                                  
  • 
                                                    < 0.4:
                                                     Limited evidence - use with caution
                                                  
    • 
                                                

                                                

                                                  
                                                   
                                                  Your network:
                                                   The distribution shows how many interactions fall into each confidence range. 
                                                  A peak at higher scores (> 0.7) indicates a more reliable network.
                                                

                                                

                                                  
                                                   
                                                  Tip:
                                                   If you see many low-confidence interactions, consider re-running the analysis with a higher confidence threshold 
                                                  (e.g., 0.7 instead of 0.4) for a more reliable but smaller network.
                                                

                                              

                                              
                                            

                                          

                                          

                                            

                                              

                                                
Degree Distribution

                                                
                                              

                                              

                                                

                                                  
                                                   Understanding Degree
                                                

                                                

                                                  What is 'Degree'?
                                                   The degree of a protein is the number of other proteins it directly interacts with.
                                                

                                                

                                                  How to interpret:
                                                

                                                
    
                                                  
  • 
                                                    High degree proteins (hubs):
                                                     Connect to many partners - often key regulators
                                                  
    • 
                                                  
  • 
                                                    Low degree proteins:
                                                     Specialized functions with few partners
                                                  
    • 
                                                  
  • 
                                                    Distribution shape:
                                                     Most biological networks show many low-degree nodes and few high-degree hubs
                                                  
    • 
                                                

                                              

                                              
                                            

                                            

                                            

                                              

                                                
Centrality Measures

                                                
                                              

                                              

                                                

                                                  
                                                   Understanding Centrality
                                                

                                                
    
                                                  
  • 
                                                    Betweenness Centrality (X-axis):
                                                     How often a protein lies on shortest paths between others. 
                                                    High betweenness = critical bridge/bottleneck
                                                  
    • 
                                                  
  • 
                                                    Closeness Centrality (Y-axis):
                                                     How quickly a protein can reach all others. 
                                                    High closeness = efficient spreader
                                                  
    • 
                                                  
  • 
                                                    Bubble size:
                                                     Represents degree (number of connections)
                                                  
    • 
                                                  
  • 
                                                    Bubble color:
                                                     Yellow/green = high degree hubs; Purple = lower connectivity
                                                  
    • 
                                                

                                                

                                                  
                                                   
                                                  Pro tip:
                                                   Proteins in the top-right corner are the most influential - they're well-connected AND control information flow!
                                                

                                              

                                              
                                            

                                          

                                        

                                      

                                      

                                        

                                          

                                            

                                              

                                                
Functional Enrichment Analysis

                                                

                                                  
Running enrichment analysis...

                                                  
                                                

                                                

                                                  

                                                  

                                                  
                                                    
                                                    Download Enrichment Results
                                                  
                                                

                                              

                                            

                                          

                                        

                                      

                                      

                                        

                                          
Network Module Detection

                                          
Functional modules detected within the PPI network using community detection algorithms.

                                          

                                            
                                          

                                          

                                            

                                              
                                              Module detection was not performed or no modules were found.
                                              

                                              

                                              To enable module detection, check 'Detect network modules/clusters' in Advanced Options before running the analysis.
                                            

                                          

                                        

                                      

                                      

                                        

                                          
PPI Analysis Summary

                                          

                                            

                                              

                                                
Input Statistics

                                                

                                              

                                            

                                            

                                              

                                                
Network Properties

                                                

                                              

                                            

                                            

                                              

                                                
Analysis Parameters

                                                

                                              

                                            

                                          

                                          

                                          
Parameters Applied in This Analysis

                                          

                                          

                                          
Top Findings

                                          

                                        

                                      

                                    

                                  

                                

                              

                            

                            

                              

                            

                          

                        

                      

                    

                  

                

              

            

          

          

            

              

                
                 Systems Biology Analyses
              

              
Perform advanced network analyses independently - WGCNA, GRN, and PPI

            

            

              
Select Analysis Module

              

                

                  

                    

                      
                      
WGCNA Analysis

                      
Weighted Gene Co-expression Network Analysis

                      
    
                        
  • Identify co-expressed gene modules
    • 
                        
  • Find hub genes
    • 
                        
  • Module-trait correlations
    • 
                      

                    

                  

                

                

                  

                    

                      
                      
GRN Analysis

                      
Gene Regulatory Network Analysis

                      
    
                        
  • Transcription factor networks
    • 
                        
  • Regulatory relationships
    • 
                        
  • Master regulators
    • 
                      

                    

                  

                

                

                  

                    

                      
                      
PPI Analysis

                      
Protein-Protein Interaction Network

                      
    
                        
  • STRING database integration
    • 
                        
  • Functional modules
    • 
                        
  • Pathway connections
    • 
                      

                    

                  

                

              

            

            
            

              

                

                  

                    
                     WGCNA - Weighted Gene Co-expression Network Analysis
                  

                  
Identify co-expressed gene modules and hub genes from your expression data

                

                

                  
                   About WGCNA:
                  
WGCNA identifies groups of genes with similar expression patterns across samples. These co-expression modules often share biological functions and can reveal key regulatory mechanisms.

                

                

                  

                    
                     Step 1: Upload Your Data
                  

                  

                    

                      

                        
                        

                          
                          
                        

                        

                          

                        

                      

                    

                    

                      

                        
                        

                          
                          
                        

                        

                          

                        

                      

                    

                  

                  

                    
                     File Format Requirements:
                    
    
                      
  • Count matrix: Genes in rows, samples in columns
    • 
                      
  • First column should be gene IDs/names
    • 
                      
  • Metadata: Sample names must match count matrix columns
    • 
                    

                  

                  

                    

                      
                       Data Loaded Successfully!
                      

                      

                    

                  

                

                

                  

                    
                     Step 2: Analysis Parameters
                  

                  

                    

                      

                        

                          

                            
                          

                        

                        
Automatically calculates optimal parameters based on your dataset characteristics (sample size, gene count, variance distribution)

                      

                      

                        

                          
                        

                      

                    

                  

                  

                    

                      

                        
                         Auto-Optimization Results
                      

                      

                      These values have been automatically calculated. You can override them below if needed.
                    

                  

                  

                  

                    

                      

                        
                        

                          
                          
                        

                      

                    

                    

                      

                        

                          
                          
                        

                        

                          
                            
                             Auto-optimized based on your data
                          
                        

                      

                    

                  

                  

                    

                      

                        

                          
                          
                        

                        

                          
                            
                             Auto-optimized for module quality
                          
                        

                      

                    

                    

                      

                        

                          
                          
                        

                        

                          
                            
                             Auto-optimized for module distinctness
                          
                        

                      

                    

                  

                  

                    

                      
                    

                  

                  

                    

                      
                    

                  

                  

                    
                     Tip: 
                    With auto-optimization enabled, the tool will automatically select the best parameters for your dataset. You can still manually adjust values if needed.
                  

                

                

                  
                

                

                  

                    

                  

                

                

                  

                  

                    
                     Analysis Results
                  

                  

                    

                      

                        

                          

                            
                          

                          
Quality Score

                        

                      

                      

                        

                          

                            
                          

                          
Modules Found

                        

                      

                      

                        

                          

                            
                          

                          
Soft Power

                        

                      

                      

                        

                          

                            
                          

                          
Genes Analyzed

                        

                      

                    

                  

                  

                    
                    

                      

                        

                        

                          

                            

                              
Gene Dendrogram with Module Colors

                              
                                
                              
                              

                              
                                
                                Download Dendrogram
                              
                            

                          

                          

                            

                              
Module Sizes

                              
                                
                              
                              

                              
                                
                                Download Plot
                              
                            

                          

                        

                        

                        

                          
Module Summary Table

                          
                            
                          
                          

                          

                          

                          

                          
                            
                            Download Module Summary
                          
                        

                      

                      

                        

                        

                          
                           Hub Genes:
                           These are the most highly connected genes within each module, often serving as key regulators or biomarkers.
                        

                        

                        

                        
                          
                          Download Hub Genes
                        
                      

                      

                        

                        

                          
                           Module Eigengenes:
                           Representative expression profiles for each module, summarizing the module's overall expression pattern.
                        

                        

                        
                          
                          Download Module Eigengenes
                        
                      

                      

                        

                        

                          

                            
                             Module-Trait Correlation:
                             Shows which modules are associated with sample traits/phenotypes. Significant correlations (p < 0.05) are marked.
                          

                          
Module-Trait Relationship Heatmap

                          

                          
                            
                            Download Heatmap
                          
                          

                          

                            
Significant Module-Trait Associations

                            

                            

                            
                              
                              Download Trait Correlations
                            
                          

                        

                        

                          

                            
                             Enable trait correlation and upload metadata file to see module-trait relationships.
                          

                        

                      

                      

                        

                        

                          

                            
                             Functional Enrichment Analysis:
                             Identifies biological pathways, GO terms, and functions enriched in each module. This helps understand the biological meaning of co-expression patterns.
                          

                          

                            

                              

                                
                                

                                  
                                  
                                

                              

                            

                            

                              

                                
                                

                                  
                                  
                                

                              

                            

                            

                              

                                
                                
                              

                            

                          

                          
                          

                            
Enrichment Results Table

                            

                            

                            
                              
                              Download Enrichment Results
                            
                          

                        

                        

                          

                            
                             Enable functional enrichment analysis to identify pathways and functions in your modules.
                          

                        

                      

                      

                        

                        

                          
                           Module Network:
                           Interactive visualization of gene co-expression within selected module. Nodes = genes, edges = co-expression strength. Larger/darker nodes are hub genes (key regulators).
                        

                        

                          

                            

                              
                              

                                
                                
                              

                            

                          

                          

                            

                              

                                
                                
                              

                              
                            

                          

                          

                            

                              

                                
                                
                              

                              
                            

                          

                        

                        

                          
                            
                             Tip: 
                            Start with default threshold (0.05). If network is too dense (hard to see), increase threshold to 0.10-0.15. If network shows no edges, decrease to 0.01-0.03. Node colors: darker = hub genes, lighter = regular genes.
                          
                        

                        

                      

                      

                        

                        

                          
                           Analysis Diagnostics:
                           Quality metrics and diagnostic plots for the WGCNA analysis.
                        

                        

                          

                            

                              
Soft Power Selection

                              

                              
The soft power threshold determines network construction sensitivity. Higher values create more specific connections.

                            

                          

                          

                            

                              
Analysis Quality Metrics

                              

                            

                          

                        

                      

                    

                  

                

              

            

            

              

                

                  

                    
                     GRN - Gene Regulatory Network Analysis
                  

                  
Discover transcription factors controlling your gene expression changes

                

                

                  
                   About GRN Analysis:
                  
Gene Regulatory Networks identify which transcription factors (TFs) regulate your differentially expressed genes. This helps find master regulators driving biological processes.

                

                

                  

                    
                     Step 1: Upload Your Data
                  

                  

                    

                      

                        
                        

                          
                          
                        

                        

                          

                        

                      

                    

                    

                      

                        
                        

                          
                          
                        

                        

                          

                        

                      

                      

                        

                          
                           Important: 
                          Expression file gene IDs should match your DEG file gene IDs.
                          

                          • If DEG uses gene symbols (e.g., TP53), expression file should also use symbols
                          

                          • If DEG uses Ensembl IDs (e.g., ENSG00000141510), expression file should also use Ensembl IDs
                          

                          • The tool will attempt automatic conversion, but matching IDs work best!
                        

                      

                    

                  

                  

                    
                     File Format Requirements:
                    
    
                      
  • DEG file must have: Gene names, log2FoldChange, padj/FDR columns
    • 
                      
  • Expression matrix: Genes in rows, samples in columns (optional)
    • 
                      
  • Genes can be Ensembl IDs, gene symbols, or Entrez IDs
    • 
                    

                  

                  

                    

                      
                       Data Loaded Successfully!
                      

                      

                    

                  

                

                

                  

                    
                     Step 2: Gene Selection Criteria
                  

                  

                    

                      

                        
                        
                      

                    

                    

                      

                        
                        
                      

                    

                  

                  

                    

                      

                    

                  

                

                

                  

                    
                     Step 3: Analysis Parameters
                  

                  

                    

                      

                        
                        

                          
                          
                        

                      

                    

                    

                      

                        
                        

                          
                          
                        

                      

                    

                  

                  

                    

                      

                        
                        
                      

                    

                    

                      

                        
                        
                      

                    

                  

                  

                    

                      

                        
                        
                      

                      
                        

                          Minimum TF Connections:
                          

                          • 1 = Show all TFs (comprehensive view)
                          

                          • 2-3 = Balanced (recommended for most analyses)
                          

                          • 5+ = Only major master regulators (clean publication figure)
                        

                      
                    

                    

                      

                        

                          
                        

                      

                      

                        

                          
                        

                      

                    

                  

                  

                    

                      

                        
                      

                    

                    

                      

                        
Advanced Parameters

                        

                          

                            

                              
                              
                            

                          

                          

                            

                              
                              
                            

                          

                        

                        

                          

                            

                              
                              

                                
                                
                              

                            

                          

                          

                            

                              

                                
                              

                            

                          

                        

                      

                    

                  

                  

                    
                     Smart Defaults:
                    
    
                      
  • Hybrid database combines DoRothEA (high confidence) and TFLink (comprehensive)
    • 
                      
  • Confidence 0.6 balances quality and coverage
    • 
                      
  • TF activities show which regulators are active in your conditions
    • 
                    

                  

                

                

                  
                

                

                  

                    

                  

                

                

                  

                  

                    
                     Analysis Results
                  

                  

                    

                      

                        

                          

                            
                          

                          
Master TFs

                        

                      

                      

                        

                          

                            
                          

                          
Target Genes

                        

                      

                      

                        

                          

                            
                          

                          
Regulations

                        

                      

                      

                        

                          

                            
                          

                          
Network Density

                        

                      

                    

                  

                  

                    
                    

                      

                        

                        

                          
                           Interactive Network:
                           TFs (circles) regulate target genes (squares). Node size = number of connections. Colors = up/down regulation.
                        

                        

                          

                            

                              
                              

                                
                                
                              

                            

                          

                          

                            

                              
                              
                            

                          

                          

                            

                              

                                
                              

                            

                          

                          

                            
                          

                        

                        

                        

                          

                        

                      

                      

                        

                        

                          
                           Master Regulators:
                           Key transcription factors with the most regulatory influence on your DEGs.
                        

                        

                          

                            

                              

                                
Top Master TFs by Target Count

                                
                                  
                                  Download
                                
                              

                              

                            

                          

                          

                            

                              

                                
TF Activity Scores

                                
                                  
                                  Download
                                
                              

                              

                                

                                  
                                  Heatmap Controls
                                

                                

                                  

                                    

                                      
                                      
                                    

                                  

                                  

                                    

                                      
                                      

                                        
                                        
                                      

                                    

                                  

                                

                                Select fewer TFs for clearer visualization. Use 'Across All' scaling to compare activity between samples.
                              

                              

                                

                              

                              

                                

                                  
                                  
TF activity requires expression data

                                

                              

                            

                          

                        

                        

                        

                          
Master Regulators Table

                          

                          

                          
                            
                            Download Master TFs
                          
                        

                      

                      

                        

                        

                          
                           Regulatory Relationships:
                           Complete list of TF-target gene regulations with confidence scores.
                        

                        

                          

                            

                              
                              

                                
                                
                              

                            

                          

                          

                            

                              
                              

                                
                                
                              

                            

                          

                          

                            

                              
                              
                            

                          

                        

                        

                        

                        
                          
                          Download All Regulations
                        
                      

                      

                        

                        

                          

                            
                             Functional Enrichment:
                             Biological pathways and functions enriched in TFs and their target genes.
                          

                          

                            

                              

                                
                                

                                  
                                  
                                

                              

                            

                            

                              

                                
                                
                              

                            

                          

                          

                            
Enrichment Plot

                            

                          

                          

                            
Enrichment Results Table

                            

                            

                            
                              
                              Download Enrichment Results
                            
                          

                        

                        

                          

                            
                             Enable pathway enrichment in analysis parameters to see functional insights.
                          

                        

                      

                      

                        

                        

                          
GRN Analysis Summary Report

                          

                        

                        

                        
                          
                          Download Complete Report (PDF)
                        
                      

                    

                  

                

              

            

            

              

                

                  

                    
                     PPI - Protein-Protein Interaction Network
                  

                  
Explore functional protein interactions using STRING database

                

                

                  
                   About PPI:
                  
Protein-Protein Interaction networks reveal functional relationships between proteins, helping identify functional modules and pathway connections.

                

                

                  

                    
                     Step 1: Upload Your Data
                  

                  

                    

                      

                        
                        

                          
                          
                        

                        

                          

                        

                      

                    

                  

                  

                    

                    OR
                    

                  

                  

                    
                    
                  

                  
                  

                    
                     File Format Requirements:
                    
    
                      
  • One gene/protein per line, OR
    • 
                      
  • CSV file with gene IDs in the first column
    • 
                      
  • Gene symbols or Ensembl/Entrez IDs accepted
    • 
                    

                  

                

                

                  

                    
                     Step 2: Analysis Parameters
                  

                  

                    

                      

                        
                        

                          
                          
                        

                      

                    

                    

                      

                        
                        
                      

                    

                    

                      

                        
                        
                      

                    

                  

                  

                    

                      

                        

                          
                        

                      

                      

                        

                          
                        

                      

                    

                    

                      

                        
                        

                          
                          
                        

                      

                      

                        

                          
                        

                      

                    

                  

                  

                    
                     Tip: 
                    Higher confidence scores (0.7-0.9) show only well-established interactions. Lower scores (0.15-0.4) include predicted interactions.
                  

                

                

                  
                

                

                  

                    

                  

                

                

                  

                  

                    
                     Analysis Results
                  

                  

                    
                    

                      

                        

                        

                          

                            

                              
Network Statistics

                              

                                

                                  

                                    

                                      
                                    

                                    
Proteins

                                  

                                

                                

                                  

                                    

                                      
                                    

                                    
Interactions

                                  

                                

                                

                                  

                                    

                                      
                                    

                                    
Avg. Degree

                                  

                                

                              

                            

                          

                        

                        

                        

                      

                      

                        

                        
Proteins with the highest number of interactions

                        

                        

                        
                          
                          Download Hub Proteins
                        
                      

                      

                        

                        

                          
                           About Functional Clusters:
                          
Proteins grouped by community detection algorithms. Proteins within the same cluster tend to work together in biological processes.

                        

                        

                        

                        

                      

                      

                        

                        
Enriched pathways in the PPI network

                        

                        

                        

                        

                        
                          
                          Download Pathway Results
                        
                      

                      
                      

                        

                        
Multiple centrality measures for key protein identification

                        

                        

                        

                      

                      

                        

                        

                          
                           About Module Enrichment:
                          
Enriched pathways for each detected functional module. This shows what biological processes each cluster is involved in.

                        

                        

                          

                            

                          

                          

                            

                          

                        

                        

                        

                      

                      

                        

                        
FDA-approved drugs targeting proteins in your network

                        

                        

                        
                          
                          Download Drug Target Info
                        
                      

                    

                  

                

                

                  

                    

                      

                        

                          
                        

                        
Ready to Analyze

                        
Click 'Run Analysis' in the sidebar to build the network.

                      

                    

                    

                      

                        
                        

                          

                            

                            

                              

                                

                                  

                                    

                                  

                                  

                                    

                                      

                                        
                                          
                                           Scroll to zoom, drag to pan, click nodes to select.
                                        
                                      

                                      

                                        
                                        
                                          
                                          Save Image
                                        
                                      

                                    

                                  

                                

                              

                              

                                

                                  

                                    Network Statistics
                                  

                                  

                                    

                                  

                                

                                

                                  

                                    

                                      ⭐ Top Hub Proteins
                                      Connections
                                    

                                  

                                  

                                    

                                  

                                

                              

                            

                          

                          

                            

                            

                              

                                

                                  

                                    
                                     Network Modules
                                  

                                  
Dense clusters of interacting proteins often represent protein complexes or functional units.

                                  

                                  

                                  

                                  

                                

                              

                              

                                

                                  

                                    

                                      
                                       Pathway Enrichment
                                    

                                    

                                      

                                        
                                        

                                          
                                          
                                        

                                      

                                    

                                  

                                  

                                    

                                      
                                      

                                        
                                        
                                      

                                    

                                  

                                  

                                  

                                

                              

                            

                          

                          
                        

                      

                    

                  

                

              

            

          

          
          

            

              

                
                 TCGA RNA-seq Analysis
              

              
Analyze differential expression and clinical data from The Cancer Genome Atlas

            

            

              

                
                
1. Select Project

                
Choose a TCGA cancer cohort

              

              

                
                
2. Set Parameters

                
Define analysis and significance thresholds

              

              

                
                
3. Run Analysis

                
Execute the DEG and pathway analysis

              

              

                
                
4. Explore Results

                
Visualize DEGs, pathways, and QC plots

              

            

            

              

                
              

              

                

                  
                  

                    

                      

                        

                          
Sample Statistics

                        

                      

                      

                        

                          

                            
Vital Status

                            

                          

                        

                        

                          

                            
Sample Types

                            

                          

                        

                      

                      

                        

                          

                            
Tissue Distribution

                            

                          

                        

                        

                          

                            
Analysis Information

                            

                          

                        

                      

                      

                        

                          
Quality Control Plot

                          

                            

                          

                        

                      

                    

                    

                      

                        

                          
Interactive Volcano Plot

                          
                          
                            
                            Download Plot
                          
                        

                      

                      

                      

                        

                          
Differential Expression Results

                          

                            
                            

                              

                                

                💡 Tip: Click on any gene row to view detailed information from OpenTargets database.

                                
                                  
                                  Download All Results
                                
                                

                                

                                

                              

                              

                                

                💡 Tip: Click on any gene row to view detailed information from OpenTargets database.

                                
                                  
                                  Download Upregulated
                                
                                

                                

                                

                              

                              

                                

                💡 Tip: Click on any gene row to view detailed information from OpenTargets database.

                                
                                  
                                  Download Downregulated
                                
                                

                                

                                

                              

                            

                          

                        

                      

                    

                    
                    

                      

                        

                          

                            
Analysis Settings

                            

                              

                                

                                  
                                  

                                    
                                    
                                  

                                

                              

                              

                                

                                  
                                  

                                    
                                    
                                  

                                

                              

                            

                          

                        

                      

                      

                        

                          

                            
Enrichment Results Filtering

                            

                              

                                

                                  
                                  
                                

                              

                              

                                

                                  
                                  

                                    
                                    
                                  

                                

                              

                            

                            
                          

                        

                      

                      

                        

                          

                            

                              

                            

                            
Analysis in progress...

                          

                          

                            
                            

                              
                              

                                

                                  

                                

                              

                              

                                

                                  

                                    

                                      

                                        
                                        

                                          
                                          
                                        

                                      

                                    

                                    

                                      

                                        
                                        
                                      

                                    

                                  

                                  
                                    
                                    Download Plot
                                  
                                  

                                

                              

                              

                                

                                  

                                    

                                      

                                        
                                        

                                          
                                          
                                        

                                      

                                    

                                    

                                      

                                        
                                        
                                      

                                    

                                    

                                      
                                        
                                        Download PDF
                                      
                                    

                                    

                                      
                                        
                                        Download PNG
                                      
                                    

                                  

                                  

                                    

                                  

                                

                              

                            

                          

                        

                      

                    

                  

                

              

            

          

          

            

              

                
                 Single Gene Expression & Survival Analysis
              

              
Investigate the expression and clinical significance of individual genes within TCGA cohorts

            

            

              

                
                
1. Select Gene & Cancer

                
Choose a TCGA cohort and a gene of interest

              

              

                
                
2. Choose Analysis

                
Select either Box Plot or Survival Analysis

              

              

                
                
3. Run Analysis

                
Generate plots and statistical summaries

              

              

                
                
4. Interpret Results

                
Assess expression differences and survival impact

              

            

            

              
              

                

                  

                    

                      
                    

                    

                      

                        
                        

                          
                          

                            

                          

                        

                      

                    

                  

                

                

                  

                    

                      
                    

                    

                      

                        
                        

                          
                          
                          

                            

                          

                        

                      

                    

                  

                

              

            

          

          

            
            

              

                
                 Gene Set Enrichment Analysis (GSEA)
              

              
Discover biological themes, pathways, and processes in your ranked gene lists

            

            

              

                
                
1. Upload Data

                
Provide a pre-ranked list or expression data

              

              

                
                
2. Select Databases

                
Choose from KEGG, GO, Reactome, and more

              

              

                
                
3. Run Analysis

                
Execute GSEA against selected databases

              

              

                
                
4. Explore Results

                
Visualize enriched pathways and gene sets

              

            

            

              

                
              

              

                

                  

                    
                    Analysis in progress... This may take a few minutes.
                  

                

                

                  

                    

                  

                  

                    
                    

                      

                        

                        

                          

                            
                              
                              Download All Results
                            
                          

                          

                            

                              Gene Display Options:
                              

                              Preview: First 5 genes
                              Full: All genes (toggle column)
                            

                          

                          

                            

                              

                            

                          

                        

                        

                        

                          

                            
                            Tip: 
                            Use the 'Show/Hide Columns' button above the table to toggle the 'All Core Genes' column for full gene lists.
                          

                        

                        

                      

                      
                      
                      

                        

                        

                          

                            

                              

                                
                                Database Comparison: 
                                Compare pathway enrichment results across different databases to identify the most robust findings.
                              

                            

                            

                            

                            

                          

                        

                      

                      

                        

                        

                          

                            
                            Plot Types:
                            

                            • 
                            Enrichment Plot: 
                            Classic GSEA running enrichment score plot
                            

                            • 
                            Dot Plot: 
                            Pathway significance vs enrichment score
                            

                            • 
                            Bar Plot: 
                            Pathway significance levels
                          

                        

                        

                          

                            

                              
                              

                                
                                
                              

                            

                          

                          

                            

                              

                                
                                

                                  
                                  
                                

                              

                            

                            

                              

                                

                                  

                                    
                                    
                                  

                                

                                

                                  

                                  

                                    

                                      
                                    

                                  

                                

                              

                            

                          

                          

                            
                              
                              Download Plot
                            
                            

                            

                            
Download formats: PNG, PDF available

                          

                        

                        

                        

                          

                            
GSEA Enrichment Score Plot

                            
Shows the running enrichment score and gene hit locations

                          

                          

                            
Dot Plot - Pathway Overview

                            
Size = gene count, Color = significance, X-axis = enrichment direction

                          

                          

                            
Bar Plot - Pathway Significance

                            
Bar height = -log10(adjusted p-value), Color = regulation direction

                          

                          

                        

                      

                      

                        

                        

                          

                            

                              
                              
                            

                          

                          

                            

                              
                              

                                
                                
                              

                            

                          

                          

                            

                              
                              
                            

                          

                          

                            

                              
                            

                            

                              

                                
                                

                                  
                                  
                                

                              

                              
                                
                                Download Network